Bootstrap Aggregator#

Bootstrap Aggregating (or bagging for short) is a model averaging technique designed to improve the stability and performance of a user-specified base estimator by training a number of them on a unique bootstrapped training set sampled at random with replacement. Bagging works especially well with estimators that tend to have high variance by controlling the variance through averaging.

Interfaces: Estimator, Learner, Parallel, Persistable

Data Type Compatibility: Depends on base learner


# Param Default Type Description
1 base Learner The base learner.
2 estimators 10 int The number of base learners to train in the ensemble.
3 ratio 0.5 float The ratio of samples from the training set to randomly subsample to train each base learner.


use Rubix\ML\BootstrapAggregator;
use Rubix\ML\Regressors\RegressionTree;

$estimator = new BootstrapAggregator(new RegressionTree(10), 300, 0.2);

Additional Methods#

This meta estimator does not have any additional methods.


  • L. Breiman. (1996). Bagging Predictors.